Research On Fluctuation Of Housing Price, Housing Bubble's Detection And Its Spatial Contagion In China

The fluctuation of housing price, especially housing price remains rapidly growing in some cities, usually brings the panic and debate on the existence of housing bubble. The housing market is a typical regional market. At present the housing market in China develops unbalanced and seriously differentiated. The housing price and housing bubbles are obviously different in different citiess. With the urbanization process speeding up and the capital flowing smoothly among regions, the increasing housing price in different cities appears certain spatial linkage. This phenomenon not only exacerbates the fluctuation of the housing price and the instability of housing market, but also makes the government's regulating policies on the housing market more difficult. Therefore, this paper focuses on housing price and housing bubble, analyzes causes of housing price fluctuation, compares the existing detection methods theoretically and empirically, tests the bubbles in China's cities with market supply-demand method, studies the contagion effects among regions.At first, based on the perspective of elements flow, this paper studies the fluctuation of housing price from the aspects of population migration, capital flow and expectation. Using the data of 70 cities in China from 2000 to 2013, this paper constructs the panel VAR model to study the dynamic relation among population migration, local public expenditures and housing price. The empirical results show that there is obvious two-way positive interaction between local public expenditures and housing price. Population migration has little obvious effect on local public expenditures and housing price. The fluctuation of population migration mainly comes from itself, while local public expenditures and housing price have no obvious influence on it. Using the panel data of 35 cities in China from 2000 to 2013, this paper investigates the relationship between the international capital flow and housing price based on different model specifications and different instrumental variable methods. The empirical results show that international capital flow has a significant positive effect on housing price. Using the data of 35 cities in China from 2006 to 2013, this paper studies the effects of expectation on housing price and its mechanism using real estate confidence index obtained by Google search, which is the proxy variable of expectation. The empirical result shows that expectation can affect housing price fluctuation not only because including fundamental expectation, but also because including animal spirit. Animal spirit affects the fundamental variables by changing decisions of economic agents and finally leads to housing price fluctuation.To investigate whether the fluctuation of housing price brings housing bubble, this paper then studies the detection of housing bubble including the definition, causes and harm of housing bubble, the detection methods of housing bubble and the empirical test of housing bubble.From the aspect of definition, causes and harm of housing bubble, this paper defines the housing bubble as the phenomenon of increasing price brought by housing price deviates from its basic value during a certain period. The causes of housing bubble are complex. Macroscopic factors such as the housing supply shortage caused by the scarcity of land, the international capital flows lead by financial liberalization, the excessive financial support of financial industry, the changes of macroeconomic policies and the lack of financial regulation all promote the emergence and development of housing bubble. The formation of housing bubble is also dependent on microscopic factors such as market participants'expectation and speculation, the positive feedback effect, information asymmetry and the herd behavior. The housing bubble does harm to China's economy. It not only increases the risk of real estate finance and leads to financial crisis, but also reduces the efficiency of resource allocation, leads to loss of social welfare and widens the gap between the rich and the poor.From the aspect of detection methods of housing bubble, this paper analyzes the existing detection methods theoretically and empirically. The existing three kinds of detection methods includes the index method, the statistical test method and theoretical price method, this paper chooses seven of them-the simple index method, the index system method, the efficiency coefficient method, the comprehensive index method, the unit root and co-integration test, the marginal returns approach and the market supply-demand method, to detect empirically Shanghai's housing bubble in 2003-2011. The results show that the selection of detection methods and the differences of bubble setting standards influence the result of housing bubbles. The results obtained from indicators of the simple index methods are different and even opposite. The unit root and co-integration test can only detect the bubble but cannot measure it. The fluctuation direction of the bubbles measured by the other five methods is fundamentally consistent, while a slight difference in the fluctuation range.From the aspect of the empirical test of housing bubble, this paper detects the housing bubbles based on data of 59 metropolises in China from 2000 to 2013 using the market supply-demand method. The results indicate that the fluctuation of housing price mainly derives from the changes of the basic supply-demand factors. The housing price has positive correlation with population, income, housing investment, housing costs and has negative correlation with unemployment rate and construction area. Then this paper analyzes the characteristics of the urban housing bubble and finds that each city's housing bubble outbreaks in different time; housing bubbles are obvious and volatile in the second and third-tier cities while are not necessarily obvious and relatively stable in the first-tier cities with high price; housing bubbles aggregate among different cities.To further investigate the spatial interaction of housing bubbles among different cities, this paper studies the spatial contagion of housing bubbles based on the data of detected bubbles and the dynamic spatial panel model of housing bubble. The results show that loose financial loan will intensify housing bubbles. The housing bubbles are persistent and will not burst immediately. There exist significant positive contemporaneous contagion effect and negative inter-temporal contagion effect. In the contemporaneous contagion effect, the economy similarity has no obvious influence, while the geographical proximity and credit similarity both play a role with the former dominated. In the inter-temporal contagion effect, the geographical proximity has no obvious influence, while the economy similarity and credit similarity both play a role with the later dominated.This paper studies issues of the fluctuation of housing price, the detection of housing bubble, the contagion effect of housing bubble, and finds that there are various causes of fluctuation of housing price; there exists housing bubbles in some cities in China and the bubbles are contagious among different cities. The conclusions in this paper can help for further understanding the fluctuation of housing price and housing bubbles, as well as provide certain suggestions for government's regulation policies of housing market. When regulating housing price and housing bubble, the government should properly guide the population migration, capital flows and expectations stabilize the market confidence.When regulating and controlling the housing price and housing bubble, the government should focus on the fundamentals and credit policies. At the same time, the government should not only focus on housing price and housing bubbles in the first-tier cities, but also pay attention to housing price and housing bubbles in the second-tier and third-tier cities. The government should implement differentiated regulating and controlling policies, make different policies according to different cities and make different control according to different classifications, to avoid the housing bubbles spread to the nationwide.